In recent years there has been a resurgence in the field of artificial intelligence (“AI”), with a rising trend of its application to automotive. However, one of the main obstacles facing the integration of proper AI systems into vehicles is undoubtedly the cost and complexity of the hardware which is required to analyze and process the abundance of raw data in real-time—a mandatory requirement in such applications. While there has been a lot of advancement with regard to GPU technologies to facilitate backend training of huge neural networks on big data, the installation of such costly and sophisticated hardware inside the vehicle is not yet commercially viable.
However, the known AI approaches still have several drawbacks which make their implementation in certain applications infeasible. Despite the huge success of Convolutional Neural Networks (CNNs), their primary dependence on learning from large amount of data points (for e.g., 1 mil.), their lengthy training times, computational cost and complexity are all big overheads when integrating such systems into the automotive. So far CNNs which are used to estimate motion employ stereo feeds, which delays the inference time, increases the computational burden and general cost. It also might be a design challenge to implement 2 cameras instead of 1. In the case of vehicle implementation, an additional design difficulty may arise when GPUs are necessary. The GPUs, which are standard machines on which to run a CNN job, require complex cooling systems for the heat they generate while consuming significant amount of power in an automotive environment. For example, a simple Intel ROS PC, weighing 12 kg, draws 300 W of power and is already pushing the feasibility limit when used in a car. Therefore, there is a need in the art for a system which is much simpler, yet equally or more accurate, with a much faster computation time, lower power consumption, running on low grade hardware and which would not add much to the cost or heating overheads of a commercial vehicle.